{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "#?KMeans\n",
    "kmeans = KMeans(n_clusters = 3, init = 'k-means++', random_state = 123)\n",
    "y_kmeans = kmeans.fit_predict(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 2,\n",
       "       0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0,\n",
       "       2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vABCRw4DHROQlVf39gDZ3A3eD80zB6z4Yky5ZN9/fY7nab7NP1KAQGDbSkPeD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f27630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(iris.data[y_kmeans == 0, 2], iris.data[y_kmeans == 0, 3], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(iris.data[y_kmeans == 1, 2], iris.data[y_kmeans == 1, 3], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(iris.data[y_kmeans == 2, 2], iris.data[y_kmeans == 2, 3], s = 100, c = 'green', label = 'Cluster 3')\n",
    "\n",
    "plt.title('Clusters of Iris')\n",
    "plt.xlabel('Petal.Length')\n",
    "plt.ylabel('Petal.Width')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.85      ,  3.07368421,  5.74210526,  2.07105263],\n",
       "       [ 5.006     ,  3.418     ,  1.464     ,  0.244     ],\n",
       "       [ 5.9016129 ,  2.7483871 ,  4.39354839,  1.43387097]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2TEwzgEIeIL9m3RiH26CQFwvtezwe++3W5D23OQAJ5woU6Px848ibZwrG5Du3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\njDEBFhSMMcYEWFAwxhgT8P8BSqwvpYPvv2YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb8074e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(iris.data[y_kmeans == 0, 2], iris.data[y_kmeans == 0, 3], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(iris.data[y_kmeans == 1, 2], iris.data[y_kmeans == 1, 3], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(iris.data[y_kmeans == 2, 2], iris.data[y_kmeans == 2, 3], s = 100, c = 'green', label = 'Cluster 3')\n",
    "\n",
    "plt.scatter(kmeans.cluster_centers_[:, 2], kmeans.cluster_centers_[:, 3], s = 100, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Clusters of Iris')\n",
    "plt.xlabel('Petal.Length')\n",
    "plt.ylabel('Petal.Width')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "dataset = pandas.read_csv('data/customers.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, [3, 4]].values\n",
    "#X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n",
    "y_kmeans = kmeans.fit_predict(X)\n",
    "#y_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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CBRYBn3vbL52sZutc84eQ+XbavJAI3vTXzNlIU2djEdvoxHbO5mleZQiX80Dja80XCyEy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\nTVISXEAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb73f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Standard')\n",
    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Traditional')\n",
    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Normal')\n",
    "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Youth')\n",
    "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'TA')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Clusters of customers')\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
